The present invention generally relates to automated target recognition and, more specifically, relates to an automated system and method for detecting anomalous objects in images with a tractable false alarm rate.
Automated target recognition (ATR) systems and processes may be employed in image analysis to determine if an image includes a specific object or target, such as a fingerprint. These systems generally comprise software; a database or directory of known target images; and a target image; i.e., an image having one or more areas to be analyzed for determination of the presence or absence targets. Generally, the system invokes the software to search the target image and compares areas suspected of having targets to known target images in the database. The known target images may comprise one or more object or target prototypes acquired, for example, from camera, infrared scanners, stored images, satellites, and other sources. Various methods may be employed for the search function, including intensity methods and feature-based methods.
Certain ATR systems include pre-processing functions to identify candidate portions of the target image that potentially contain a match to a known image. The system then focuses on the candidate portion for further processing, excluding the remaining areas of the target image. Exemplary applications of ATR techniques include identification of fingerprint, hand or retina; identification of a specific face from group photographs; and identification of types and numbers of enemy aircraft photographed using satellite technology.
Generally, ATR comprises three phases: data acquisition, data processing, and decision classification. During the data acquisition phase, a data acquisition subsystem gathers data from the physical world through a sensor and converts it to digital formats suitable for computer processing; for example, an image. During the data processing phase, or target detection phase, a data potentially contain targets. The data processing subsystem assigns a numerical value, or value, to each region or each pixel. The numerical value correlates to a potential for a specific region to contain a target or for a specific pixel to represent a target. During the decision classification phase, a decision classification subsystem dichotomizes each assigned value according to whether the value represents a target or not.
Target and background characteristics play a significant role in target image processing and classification. Certain targets comprise unique, immutable patterns and characteristics that permit absolute matching with a known image and, therefore, permit absolute classification. Fingerprints, for example, fall into this category. Further, such targets can be displayed on a uniform background, such as white paper, and absolute distinction between the fingerprint and its associated background can be made. Such distinction permits analysis of the fingerprint and exclusion of the background, thus simplifying the analysis and classification process. Other targets, such as synthetic or manufactured items, generally comprise uniform characteristics. For example, mines often exhibit highly geometrical structures, permitting the shape of such a target to be specified with near certainty.
A system used in imaged detection of the foregoing type is disclosed in U.S. Pat. No. 6,501,857 to Gotsman et al. The system uses a group of the eigenvectors having the weakest eigenvalues to select basis vectors. A second process is then performed on this group of “weakest” eigenvectors to identify a set of candidate vectors, ordered in terms of “smoothness.” The set of basis vectors is then chosen from the candidate vectors in order of smoothness, which are then applied in an image detection or image recognition process.
Another system and apparatus of the prior art is disclosed in U.S. Pat. No. 5,710,833 to Moghaddam et al., for detecting instances of a selected object or object feature in a digitally represented scene. The system and apparatus utilize analysis of probability densities to determine whether an input image (or portion thereof) represents such an instance. The invention filters images of objects that, although in some ways are similar to the object under study, fail to qualify as typical instances of that object. The invention is useful in the detection and recognition of virtually any multi-featured entity such as human faces, features thereof, and non-rigid and articulated objects such as human hands.
Various target images, however, contain varying and variegated background areas. Such images evade standard analysis and classification techniques due to an inability to absolutely distinguish a suspect target from its surrounding background. Examples of the foregoing include target images having background areas of sand, grass, clay and other natural surrounds. Analysis and classification ability degrades even further under conditions where both the potential targets and the background areas of the target image contain characteristics deviating from known images, precluding exact target classification.
Anomaly detection schemes may be employed in situations where exact target classification is not feasible. Anomaly detection methods search for contrast differences between areas in the target image rather than searching for a specific pattern within the target image, based on the assumption that a majority of the target image comprises uninteresting and similar areas. Therefore, a target occurs as an anomaly with respect to most of the target image.
Common anomaly detection algorithms are derived from standard tests of statistical hypothesis. These tests typically rely on a small number of assumptions regarding the distribution type of the samples. Statistical tests relate the observed samples in the target images to standard distributions, such as the normal distribution, F-distribution, and t-distribution.
Theoretically, the output values of the initial analysis follow one of the aforementioned distributions and selection of a threshold can be made based on a predetermined confidence level, for example 95%. The rate of false alarms; i.e., non-anomalous areas that surpass the threshold, may then be obtained by multiplying the number of samples and the difference between 100% and a confidence level. For example, in an application having a confidence level of 95% and 100 samples, the false alarm rate is gauged at five according to the foregoing formula. The detection rate may be based on the specific characteristics of the targets. In cases where very little is known about the target characteristics, thresholds are traditionally based on false alarm rates.
The FX target detection algorithm is one example of an adaptive constant false alarm rate technique using spatial and contrast information, Signature Adaptive Mine Detection At A Constant False Alarm Rate, Crosby, F. and Riley, S., Proceedings of SPIE Detection and Remediation Technologies for Mines and Minelike Targets IV, April 2001. Another example is an algorithm disclosed in Comparative Performance Analysis of Adaptive Multispectral Detectors, Yu, X., Reed, I., and Stocker, A., IEEE Transactions on Signal Processing, Vol. 41, No. 8, August 1993.
The FX algorithm is a scalar-valued function of the potentially multi-dimensional quantities of average target intensity, average background intensity, and common covariance of the background and the target. Statistically, the distributions are assumed to be normal with means μB and μT and common covariance Σ, denoted N(μB, Σ) and N(μT, Σ), respectively. The detection metric is calculated as:
      F    ⁡          (      X      )        =                              N          B                ⁢                  N          T                    N        ⁢                  (                              μ            B                    -                      μ            T                          )            T        ⁢                  ∑                  -          1                    ⁢                        (                                    μ              B                        -                          μ              T                                )                .            
The theoretical false alarm output of the FX algorithm follows the F-distribution. The specific F-distribution is determined by the number of dimensions and the number of samples used in estimating the target and background averages. FIG. 1 shows a graph having an x-axis 1 and a y-axis 2 of the F-distribution for six dimensions and 1,674 samples, according to the prior art.
Ideally, the threshold could be selected based on a graph of this type. In reality, however, achieving a certain confidence level is quite difficult. Given the same target dimensions and the same number of samples, the actual output for two sets of input can vary significantly. An example of this variability can be seen in FIGS. 2 and 3, each according to the prior art. FIG. 2 illustrates a graph having an x-axis 3 and a y-axis 4, and the graphical output from a target image having background areas of sand. The number of objects is shown along the y-axis 3 and the corresponding detection value is shown along the x-axis 4. FIG. 3 illustrates a graph having an x-axis 5, a y-axis 6, and the graphical output from a target image having background areas of grass. The number of objects is shown along the y-axis 6, and the corresponding detection values are shown along the x-axis 5. As can be seen, FIG. 2 contains many representations of detection values over twelve, while FIG. 3 has none. Although both outputs have the same small number of true targets, selection of a threshold value of twelve, for example, results in many false alarms in the grass case (FIG. 2) yet results in no false alarms in the sand case (FIG. 3).
Further, common anomaly detection methods of the prior art employed to analyze images having targets larger than one pixel typically process each output pixel in the target-sized area, resulting in undesirable redundant classifications.
As can be seen, there is a need for a system and method of independent threshold selection on a per-image basis. Further, such a system and method preferably provide a threshold based only on a desired number of false alarms per image. Finally, there is a need for a system and method of independent threshold selection that minimizes duplicate classifications within a given area of the image.